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When Developing Cyclone Melissa swirled south of Haiti, weather expert Philippe Papin had confidence it would soon escalate to a major tropical system.

Serving as primary meteorologist on duty, he forecasted that in a single day the storm would become a category 4 hurricane and start shifting in the direction of the Jamaican shoreline. Not a single expert had previously made this confident prediction for rapid strengthening.

But, Papin possessed a secret advantage: AI technology in the form of the tech giant’s recently introduced DeepMind cyclone prediction system – released for the initial occasion in June. True to the forecast, Melissa evolved into a storm of astonishing strength that ravaged Jamaica.

Growing Dependence on AI Predictions

Forecasters are heavily relying upon Google DeepMind. On the morning of 25 October, Papin clarified in his public discussion that the AI tool was a key factor for his confidence: “Approximately 40/50 Google DeepMind ensemble members indicate Melissa becoming a most intense storm. Although I am not ready to predict that intensity at this time given path variability, that is still plausible.

“It appears likely that a phase of rapid intensification will occur as the system moves slowly over very warm sea temperatures which is the most extreme oceanic heat content in the whole Atlantic basin.”

Surpassing Conventional Models

The AI model is the pioneer artificial intelligence system focused on hurricanes, and currently the initial to beat standard weather forecasters at their specialty. Across all 13 Atlantic storms this season, Google’s model is top-performing – even beating experts on track predictions.

Melissa eventually made landfall in Jamaica at category 5 intensity, one of the strongest coastal impacts ever documented in almost 200 years of data collection across the Atlantic basin. The confident prediction probably provided people in Jamaica extra time to prepare for the disaster, possibly saving people and assets.

The Way Google’s Model Functions

Google’s model works by spotting patterns that traditional lengthy physics-based weather models may miss.

“They do it much more quickly than their physics-based cousins, and the computing power is less expensive and time consuming,” said Michael Lowry, a ex forecaster.

“What this hurricane season has proven in quick time is that the newcomer artificial intelligence systems are competitive with and, in some cases, superior than the slower traditional weather models we’ve traditionally leaned on,” Lowry said.

Understanding Machine Learning

To be sure, the system is an example of machine learning – a method that has been employed in data-heavy sciences like meteorology for a long time – and is not generative AI like ChatGPT.

AI training processes large datasets and pulls out patterns from them in a such a way that its model only requires minutes to generate an result, and can operate on a standard PC – in strong contrast to the primary systems that authorities have used for years that can take hours to run and need some of the biggest supercomputers in the world.

Professional Reactions and Upcoming Developments

Nevertheless, the reality that Google’s model could outperform previous top-tier legacy models so rapidly is truly remarkable to meteorologists who have spent their careers trying to forecast the world’s strongest storms.

“It’s astonishing,” said James Franklin, a retired forecaster. “The data is now large enough that it’s evident this is not a case of chance.”

Franklin said that while Google DeepMind is outperforming all competing systems on predicting the trajectory of hurricanes worldwide this year, similar to other systems it sometimes errs on extreme strength forecasts inaccurate. It struggled with Hurricane Erin previously, as it was similarly experiencing rapid intensification to category 5 above the Caribbean.

During the next break, he stated he intends to talk with the company about how it can make the AI results even more helpful for experts by offering extra under-the-hood data they can use to evaluate the reasons it is coming up with its answers.

“A key concern that nags at me is that although these forecasts appear highly accurate, the output of the model is essentially a black box,” remarked Franklin.

Broader Industry Trends

There has never been a commercial entity that has developed a top-level forecasting system which allows researchers a peek into its methods – in contrast to nearly all other models which are offered free to the general audience in their entirety by the governments that created and operate them.

The company is not the only one in starting to use artificial intelligence to solve challenging meteorological problems. The US and European governments are developing their respective AI weather models in the development phase – which have also shown better performance over previous traditional systems.

The next steps in AI weather forecasts seem to be startup companies taking swings at previously tough-to-solve problems such as sub-seasonal outlooks and better early alerts of tornado outbreaks and sudden deluges – and they are receiving US government funding to do so. One company, WindBorne Systems, is also deploying its proprietary atmospheric sensors to address deficiencies in the US weather-observing network.